import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import folium
from scipy.stats import ttest_ind, f_oneway
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score, classification_report, accuracy_score
from sklearn.ensemble import RandomForestClassifier
from imblearn.over_sampling import SMOTE

# Load the data from the CSV file (replace 'file_path' with the actual file path)
file_path = 'Crime_Data_from_2020_to_Present.csv'  # Change this to the actual path if different
df = pd.read_csv(file_path)

# Filter the DataFrame where "Crm Cd Desc" contains the value of interest
filtered_df = df[df['Crm Cd Desc'] == "CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT"]

# Display the filtered data
filtered_df.head(10)

# Define the specific columns you want to keep
columns_to_keep = [
    "Date Rptd", "DATE OCC", "TIME OCC", "AREA", "AREA NAME", 
    "Crm Cd", "Crm Cd Desc", "Vict Age", "Vict Sex", 
    "Premis Cd", "Premis Desc", "Weapon Used Cd", 
    "Weapon Desc", "Status", "Status Desc","LAT","LON"
]

# Filter for "Child Abuse" with specific columns
df_child_abuse_physical = filtered_df[columns_to_keep]

# Display each DataFrame separately with only the selected columns
df_child_abuse_physical.head(10)

# Filter the DataFrame for the specific columns
df_child_abuse_physical = filtered_df.loc[:, columns_to_keep]

# Define a function to format "TIME OCC" to HH:MM
def format_time_occ(time_occ):
    try:
        # Convert to a 4-character string, padding with leading zeros if necessary
        time_str = str(int(float(time_occ))).zfill(4)
        hours = int(time_str[:2])  # Extract the hour part
        minutes = int(time_str[2:])  # Extract the minute part
        return f"{hours}:{minutes:02d}"  # Format as HH:MM
    except (ValueError, TypeError):
        return "Invalid Time"

# Apply the formatting function to the "TIME OCC" column
df_child_abuse_physical['TIME OCC'] = df_child_abuse_physical['TIME OCC'].apply(format_time_occ)


# Display the first 10 rows of the filtered DataFrame
df_child_abuse_physical.head(10)

# Filter the DataFrame for the specific columns
df_child_abuse_physical = filtered_df.loc[:, columns_to_keep]

# Explicitly convert "TIME OCC" column to string type to avoid dtype conflicts
df_child_abuse_physical['TIME OCC'] = df_child_abuse_physical['TIME OCC'].astype(str)


# Define a function to format "TIME OCC" to HH:MM
def format_time_occ(time_occ):
    try:
        # Convert to a 4-character string, padding with leading zeros if necessary
        time_str = str(int(float(time_occ))).zfill(4)
        hours = int(time_str[:2])  # Extract the hour part
        minutes = int(time_str[2:])  # Extract the minute part
        return f"{hours}:{minutes:02d}"  # Format as HH:MM
    except (ValueError, TypeError):
        return "Invalid Time"

# Apply the formatting function to the "TIME OCC" column
df_child_abuse_physical.loc[:, 'TIME OCC'] = df_child_abuse_physical['TIME OCC'].apply(format_time_occ)


# Combine "DATE OCC" and "TIME OCC" into a single datetime column
df_child_abuse_physical['DATETIME OCC'] = pd.to_datetime(
    df_child_abuse_physical['DATE OCC'].str.split(' ').str[0] + ' ' + df_child_abuse_physical['TIME OCC'], 
    errors='coerce'
)

# Format the "DATETIME OCC" column to MM/DD/YYYY HH:MM
df_child_abuse_physical['DATETIME OCC'] = df_child_abuse_physical['DATETIME OCC'].dt.strftime('%m/%d/%Y %H:%M')

# Display the first 10 rows of the filtered DataFrame
df_child_abuse_physical[['DATETIME OCC'] + columns_to_keep].head(10)

# Convert 'DATETIME OCC' column to datetime format if not already
df_child_abuse_physical['DATETIME OCC'] = pd.to_datetime(df_child_abuse_physical['DATETIME OCC'], errors='coerce')

# Extract the 'Year' from 'DATETIME OCC'
df_child_abuse_physical['Year'] = df_child_abuse_physical['DATETIME OCC'].dt.year

# Filter data for years 2020 to 2023
filtered_years_data = df_child_abuse_physical[df_child_abuse_physical['Year'].between(2020, 2023)]

# Count the number of incidents per year
yearly_counts_till_2023 = filtered_years_data['Year'].value_counts().sort_index()

# Plot the yearly data as a bar graph
plt.figure(figsize=(10, 6))
plt.bar(yearly_counts_till_2023.index, yearly_counts_till_2023.values, color='skyblue')
plt.title('Yearly Child Abuse Data (2020-2023)', fontsize=16)
plt.xlabel('Year', fontsize=12)
plt.ylabel('Number of Incidents', fontsize=12)
plt.xticks(yearly_counts_till_2023.index)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

# Ensure 'DATETIME OCC' is in datetime format
df_child_abuse_physical['DATETIME OCC'] = pd.to_datetime(df_child_abuse_physical['DATETIME OCC'], errors='coerce')

# Extract the year and month from the 'DATETIME OCC' column
df_child_abuse_physical['Year-Month'] = df_child_abuse_physical['DATETIME OCC'].dt.to_period('M')

# Filter the data for years 2020 to 2023
filtered_monthly_data = df_child_abuse_physical[df_child_abuse_physical['Year-Month'].dt.year.between(2020, 2023)]

# Count the number of incidents for each month
monthly_counts = filtered_monthly_data['Year-Month'].value_counts().sort_index()

# Plot the monthly data as a bar graph
plt.figure(figsize=(12, 6))
monthly_counts.plot(kind='bar', color='lightcoral')
plt.title('Monthly Child Abuse Data (2020-2023)', fontsize=16)
plt.xlabel('Month (Year-Month)', fontsize=12)
plt.ylabel('Number of Incidents', fontsize=12)
plt.xticks(rotation=90)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

# Ensure 'DATETIME OCC' is in datetime format
df_child_abuse_physical['DATETIME OCC'] = pd.to_datetime(df_child_abuse_physical['DATETIME OCC'], errors='coerce')

# Extract the year and month from the 'DATETIME OCC' column
df_child_abuse_physical['Year-Month'] = df_child_abuse_physical['DATETIME OCC'].dt.to_period('M')

# Filter the data for years 2020 to 2023
filtered_monthly_data = df_child_abuse_physical[df_child_abuse_physical['Year-Month'].dt.year.between(2020, 2023)]

# Count the number of incidents for each month (grouped by Year-Month)
monthly_counts = filtered_monthly_data['Year-Month'].value_counts().sort_index()

# Plot the data as a line chart
plt.figure(figsize=(12, 6))
plt.plot(monthly_counts.index.astype(str), monthly_counts.values, marker='o', color='teal', linestyle='-', linewidth=2, markersize=6)
plt.title('Monthly Child Abuse Incidents (2020-2023)', fontsize=16)
plt.xlabel('Month (Year-Month)', fontsize=12)
plt.ylabel('Number of Incidents', fontsize=12)
plt.xticks(rotation=90)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

# Ensure 'DATETIME OCC' is in datetime format
df_child_abuse_physical['DATETIME OCC'] = pd.to_datetime(df_child_abuse_physical['DATETIME OCC'], errors='coerce')

# Extract the year from the 'DATETIME OCC' column
df_child_abuse_physical['Year'] = df_child_abuse_physical['DATETIME OCC'].dt.year

# Filter data for years 2020 to 2023
filtered_data_sex = df_child_abuse_physical[df_child_abuse_physical['Year'].between(2020, 2023)]

# Filter for only Male ('M') and Female ('F') victims
filtered_data_sex = filtered_data_sex[filtered_data_sex['Vict Sex'].isin(['M', 'F'])]

# Group the data by year and victim sex, then count incidents
sex_yearly_counts = filtered_data_sex.groupby(['Year', 'Vict Sex']).size().reset_index(name='Incidents')

# Plot the data using seaborn to ensure distinct colors for each sex
plt.figure(figsize=(12, 6))
sns.barplot(data=sex_yearly_counts, x='Year', y='Incidents', hue='Vict Sex', palette='Set1')

# Add labels and title
plt.title('Child Abuse Incidents by Victim Sex (2020-2023)', fontsize=16)
plt.xlabel('Year', fontsize=12)
plt.ylabel('Number of Incidents', fontsize=12)
plt.xticks(rotation=0)
plt.legend(title='Victim Sex', loc='upper right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()

# Show the plot
plt.show()

# Count the number of incidents for each victim sex (male and female)
sex_counts = df_child_abuse_physical['Vict Sex'].value_counts()

# Filter for only male and female data
sex_counts = sex_counts[sex_counts.index.isin(['M', 'F'])]

# Plot a pie chart for the sex distribution
plt.figure(figsize=(8, 8))
plt.pie(sex_counts, labels=sex_counts.index, autopct='%1.1f%%', colors=['lightblue', 'lightcoral'], startangle=90, wedgeprops={'edgecolor': 'black'})

# Add a title
plt.title('Percentage of Child Abuse Incidents by Victim Sex (Male vs Female)', fontsize=16)

# Show the plot
plt.show()

# Ensure 'DATETIME OCC' is in datetime format
df_child_abuse_physical['DATETIME OCC'] = pd.to_datetime(df_child_abuse_physical['DATETIME OCC'], errors='coerce')

# Extract the year from the 'DATETIME OCC' column
df_child_abuse_physical['Year'] = df_child_abuse_physical['DATETIME OCC'].dt.year

# Filter data for years 2020 to 2023
filtered_data_year = df_child_abuse_physical[df_child_abuse_physical['Year'].between(2020, 2023)]

# Further filter the data to include only victims under 18 and male/female victims
filtered_data_year = filtered_data_year[filtered_data_year['Vict Age'] < 18]
filtered_data_year = filtered_data_year[filtered_data_year['Vict Sex'].isin(['M', 'F'])]

# Create the violin plot with year-wise distribution of victim age
plt.figure(figsize=(12, 6))
sns.violinplot(x='Year', y='Vict Age', data=filtered_data_year, inner=None, color="white", linewidth=1, edgecolor="black", cut=0)

# Add title and labels
plt.title("Year-wise Distribution of Victim Age (Male & Female, Under 18) - Child Abuse (2020-2023)", fontsize=16)
plt.xlabel("Year", fontsize=12)
plt.ylabel("Victim Age", fontsize=12)

# Enable grid
plt.grid(True, which='both', linestyle='--', linewidth=0.5)

# Show the plot
plt.show()

# Ensure 'DATETIME OCC' is in datetime format
df_child_abuse_physical['DATETIME OCC'] = pd.to_datetime(df_child_abuse_physical['DATETIME OCC'], errors='coerce')

# Extract the year from the 'DATETIME OCC' column
df_child_abuse_physical['Year'] = df_child_abuse_physical['DATETIME OCC'].dt.year

# Filter data for years 2020 to 2023
filtered_data_sex = df_child_abuse_physical[df_child_abuse_physical['Year'].between(2020, 2023)]

# Filter for only Male ('M') and Female ('F') victims
filtered_data_sex = filtered_data_sex[filtered_data_sex['Vict Sex'].isin(['M', 'F'])]

# Further filter the data to include only victims under 18 (child abuse cases)
filtered_data_sex = filtered_data_sex[filtered_data_sex['Vict Age'] < 18]

# Create the violin plot with adjusted styles
plt.figure(figsize=(12, 6))
sns.violinplot(x='Vict Sex', y='Vict Age', data=filtered_data_sex, inner=None, color="white", linewidth=1, edgecolor="black", cut=0)

# Add title and labels
plt.title("Distribution of Victim Age by Victim Sex (Under 18) - Child Abuse (2020-2023)", fontsize=16)
plt.xlabel("Victim Sex", fontsize=12)
plt.ylabel("Victim Age", fontsize=12)

# Enable grid
plt.grid(True, which='both', linestyle='--', linewidth=0.5)

# Show the plot
plt.show()

# Filter data for years 2020 to 2023 and victims under 18
filtered_data = df_child_abuse_physical[
    (df_child_abuse_physical['Year'].between(2020, 2023)) & 
    (df_child_abuse_physical['Vict Age'] < 18)
]

# Create a map centered around Los Angeles (approx. coordinates)
map_center = [34.0522, -118.2437]  # Latitude and Longitude for Los Angeles
mymap = folium.Map(location=map_center, zoom_start=12)

# Iterate over the rows and add a marker for each crime
for index, row in filtered_data.iterrows():
    if pd.notnull(row['LAT']) and pd.notnull(row['LON']):  # Check if coordinates exist
        # Assign color based on Victim Sex
        if row['Vict Sex'] == 'M':  # Male victims
            color = 'blue'
        elif row['Vict Sex'] == 'F':  # Female victims
            color = 'red'
        else:  # Other or unknown
            color = 'gray'

        # Add a marker for each crime
        folium.CircleMarker(
            location=[row['LAT'], row['LON']],
            radius=5,
            color=color,  # Dynamic color based on victim sex
            fill=True,
            fill_color=color,
            fill_opacity=0.6,
            popup=f"Crime: {row['Crm Cd Desc']}<br>Victim Age: {row['Vict Age']}<br>Victim Sex: {row['Vict Sex']}"
        ).add_to(mymap)

# Save the map to an HTML file
mymap.save('child_abuse_crime_map.html')

# Display the map (if running in a Jupyter notebook)
mymap

# Create copies of the filtered data
male_data = filtered_data[filtered_data['Vict Sex'] == 'M'].copy()
female_data = filtered_data[filtered_data['Vict Sex'] == 'F'].copy()

# Offset years slightly for better separation
male_data['Year Offset'] = male_data['Year'] - 0.1  # Shift male points slightly left
female_data['Year Offset'] = female_data['Year'] + 0.1  # Shift female points slightly right

# Fit linear regression for males
male_model = LinearRegression()
male_X = male_data[['Year']]
male_y = male_data['Vict Age']
male_model.fit(male_X, male_y)
male_pred = male_model.predict(male_X)

# Fit linear regression for females
female_model = LinearRegression()
female_X = female_data[['Year']]
female_y = female_data['Vict Age']
female_model.fit(female_X, female_y)
female_pred = female_model.predict(female_X)

# Plot the data and regression lines
plt.figure(figsize=(12, 6))

# Scatter plot for males
plt.scatter(male_data['Year Offset'], male_data['Vict Age'], color='blue', alpha=0.7, label='Male Victims')

# Scatter plot for females
plt.scatter(female_data['Year Offset'], female_data['Vict Age'], color='red', alpha=0.7, label='Female Victims')

# Regression line for males
plt.plot(male_data['Year'], male_pred, color='blue', linewidth=2, label='Male Regression Line')

# Regression line for females
plt.plot(female_data['Year'], female_pred, color='red', linewidth=2, label='Female Regression Line')

# Customize the plot
plt.title('Linear Regression of Victim Age Over Time (Male vs Female, Under 18)', fontsize=16)
plt.xlabel('Year', fontsize=12)
plt.ylabel('Victim Age', fontsize=12)

# Set y-axis ticks to integers
min_age = int(filtered_data['Vict Age'].min())
max_age = int(filtered_data['Vict Age'].max())
plt.yticks(range(min_age, max_age + 1))

plt.grid(True, linestyle='--', alpha=0.5)
plt.legend(title='Legend', loc='upper left')
plt.tight_layout()

# Show the plot
plt.show()

# Calculate descriptive statistics
summary = filtered_data.groupby(['Year', 'Vict Sex']).agg(
    Avg_Age=('Vict Age', 'mean'),
    Median_Age=('Vict Age', 'median'),
    Count=('Vict Age', 'count')
).reset_index()

# Calculate descriptive statistics for male and female separately
male_summary = summary[summary['Vict Sex'] == 'M']
female_summary = summary[summary['Vict Sex'] == 'F']

# Plot line plots for males and females
plt.figure(figsize=(10, 6))

# Male data
sns.lineplot(data=male_summary, x='Year', y='Count', label='Male Victims', color='blue')

# Female data
sns.lineplot(data=female_summary, x='Year', y='Count', label='Female Victims', color='red')

# Customize the plot
plt.title('Number of Victims Over Time by Gender', fontsize=16)
plt.xlabel('Year', fontsize=12)
plt.ylabel('Number of Victims', fontsize=12)
plt.grid(True, linestyle='--', alpha=0.5)
plt.legend(title='Victim Sex')
plt.tight_layout()

# Show the plot
plt.show()

male_ages = filtered_data[filtered_data['Vict Sex'] == 'M']['Vict Age']
female_ages = filtered_data[filtered_data['Vict Sex'] == 'F']['Vict Age']

t_stat, p_val = ttest_ind(male_ages, female_ages)

print(f"T-Statistic: {t_stat}, P-Value: {p_val}")
if p_val < 0.05:
    print("Significant difference in average ages between male and female victims.")
else:
    print("No significant difference in average ages.")

T-Statistic: -8.896874050719962, P-Value: 9.368738598309197e-19
Significant difference in average ages between male and female victims.

years = filtered_data['Year'].unique()
age_groups = [filtered_data[filtered_data['Year'] == year]['Vict Age'] for year in years]

f_stat, p_val = f_oneway(*age_groups)

print(f"F-Statistic: {f_stat}, P-Value: {p_val}")
if p_val < 0.05:
    print("Significant age trend over time.")
else:
    print("No significant age trend over time.")

F-Statistic: 1.8226381047157993, P-Value: 0.14079957873073476
No significant age trend over time.

# Filter data to include only male ('M') and female ('F') victims
filtered_data = filtered_data[filtered_data['Vict Sex'].isin(['M', 'F'])]

# Prepare data: One-hot encode 'Vict Sex' (drop 'F' as the reference category)
X = pd.get_dummies(filtered_data[['Vict Sex']], drop_first=True)
y = filtered_data['Vict Age']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Evaluate the model
y_pred = model.predict(X_test)
r2 = r2_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)

# Format coefficients as standard Python floats
coefficients = {feature: float(coeff) for feature, coeff in zip(X.columns, model.coef_)}

# Display results
print(f"Intercept: {model.intercept_:.4f}")
print(f"Coefficients: {coefficients}")
print(f"R^2 Score: {r2:.4f}")
print(f"Mean Squared Error: {mse:.4f}")

Intercept: 11.5639
Coefficients: {'Vict Sex_M': -1.413271541145068}
R^2 Score: 0.0164
Mean Squared Error: 21.3403

# Create a DataFrame for predictions (male and female)
regression_data = pd.DataFrame({
    'Vict Sex_M': [0, 1],  # Female (0), Male (1)
    'Predicted Vict Age': [model.intercept_, model.intercept_ + model.coef_[0]]
})

# Scatter plot for actual data points
plt.figure(figsize=(8, 6))
plt.scatter(
    X['Vict Sex_M'], y,
    alpha=0.6, color='gray', label='Actual Data'
)

# Regression line (predicted values)
plt.plot(
    regression_data['Vict Sex_M'], regression_data['Predicted Vict Age'],
    color='red', label='Regression Line', linewidth=2
)

# Customize the plot
plt.title('Regression: Victim Age by Gender', fontsize=16)
plt.xticks([0, 1], ['Female', 'Male'], fontsize=12)  # Label categories
plt.xlabel('Victim Sex', fontsize=12)
plt.ylabel('Victim Age', fontsize=12)
plt.grid(True, linestyle='--', alpha=0.5)
plt.legend()
plt.tight_layout()

# Show the plot
plt.show()

# Filter data to include only male ('M') and female ('F')
filtered_data = filtered_data[filtered_data['Vict Sex'].isin(['M', 'F'])]

# Prepare data
X = filtered_data[['Year', 'Vict Age']]
y = filtered_data['Vict Sex'].apply(lambda x: 1 if x == 'M' else 0)  # Binary encoding: Male=1, Female=0

# Handle class imbalance using SMOTE
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.3, random_state=42)

# Hyperparameter tuning with GridSearchCV
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20],
    'min_samples_split': [2, 5],
    'min_samples_leaf': [1, 2]
}
grid_search = GridSearchCV(RandomForestClassifier(random_state=42), param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)

# Best model
best_clf = grid_search.best_estimator_

# Evaluate the model
y_pred = best_clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)

# Feature importance
feature_importances = pd.Series(best_clf.feature_importances_, index=X.columns)

# Display results
print(f"Best Parameters: {grid_search.best_params_}")
print(f"Accuracy: {accuracy:.4f}")
print("Classification Report:")
print(report)
print("\nFeature Importances:")
print(feature_importances.sort_values(ascending=False))

plt.figure(figsize=(8, 6))
feature_importances.sort_values(ascending=False).plot(kind='bar', color='skyblue')
plt.title('Feature Importances', fontsize=16)
plt.ylabel('Importance', fontsize=12)
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()

Best Parameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}
Accuracy: 0.5574
Classification Report:
              precision    recall  f1-score   support

           0       0.54      0.53      0.54       496
           1       0.57      0.58      0.58       532

    accuracy                           0.56      1028
   macro avg       0.56      0.56      0.56      1028
weighted avg       0.56      0.56      0.56      1028


Feature Importances:
Vict Age    0.864202
Year        0.135798
dtype: float64

	DR_NO	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Rpt Dist No	Part 1-2	Crm Cd	Crm Cd Desc	...	Status	Status Desc	Crm Cd 1	Crm Cd 2	Crm Cd 3	Crm Cd 4	LOCATION	Cross Street	LAT	LON
207	231906446	02/28/2023 12:00:00 AM	02/17/2020 12:00:00 AM	1200	19	Mission	1974	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	AA	Adult Arrest	627.0	NaN	NaN	NaN	9000 WILLIS AV	NaN	34.2337	-118.4553
246	220506019	02/01/2022 12:00:00 AM	02/01/2020 12:00:00 AM	1200	5	Harbor	507	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	AO	Adult Other	627.0	NaN	NaN	NaN	1400 W 227TH ST	NaN	33.8201	-118.3015
495	220917252	12/13/2022 12:00:00 AM	12/21/2020 12:00:00 AM	1800	9	Van Nuys	916	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	AO	Adult Other	627.0	NaN	NaN	NaN	VANOWEN	WOODMAN	34.1939	-118.4313
556	221222580	10/30/2022 12:00:00 AM	06/01/2020 12:00:00 AM	1500	12	77th Street	1267	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	AO	Adult Other	627.0	NaN	NaN	NaN	800 W 83RD ST	NaN	33.9636	-118.2871
727	221715285	10/26/2022 12:00:00 AM	01/01/2020 12:00:00 AM	1200	17	Devonshire	1708	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	IC	Invest Cont	627.0	NaN	NaN	NaN	12000 SUSAN DR	NaN	34.2880	-118.4971
750	231008955	05/09/2023 12:00:00 AM	01/09/2020 12:00:00 AM	1	10	West Valley	1028	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	AO	Adult Other	627.0	NaN	NaN	NaN	16500 VANOWEN ST	NaN	34.1939	-118.4924
771	232104840	01/18/2023 12:00:00 AM	12/01/2020 12:00:00 AM	1200	21	Topanga	2101	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	AO	Adult Other	627.0	NaN	NaN	NaN	9300 GLADE AV	NaN	34.2393	-118.6090
855	221610929	07/30/2022 12:00:00 AM	07/01/2020 12:00:00 AM	1200	16	Foothill	1695	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	AO	Adult Other	627.0	NaN	NaN	NaN	11000 ARMINTA ST	NaN	34.2139	-118.3703
887	221319458	10/17/2022 12:00:00 AM	09/01/2020 12:00:00 AM	1200	13	Newton	1367	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	IC	Invest Cont	627.0	NaN	NaN	NaN	31ST ST	GRIFFITH AV	33.9993	-118.2432
996	220309997	05/02/2022 12:00:00 AM	11/01/2020 12:00:00 AM	1200	3	Southwest	393	2	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	...	AA	Adult Arrest	627.0	NaN	NaN	NaN	4100 LEIMERT BL	NaN	34.0098	-118.3242

	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Crm Cd	Crm Cd Desc	Vict Age	Vict Sex	Premis Cd	Premis Desc	Weapon Used Cd	Weapon Desc	Status	Status Desc	LAT	LON
207	02/28/2023 12:00:00 AM	02/17/2020 12:00:00 AM	1200	19	Mission	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	F	501.0	SINGLE FAMILY DWELLING	301.0	BELT FLAILING INSTRUMENT/CHAIN	AA	Adult Arrest	34.2337	-118.4553
246	02/01/2022 12:00:00 AM	02/01/2020 12:00:00 AM	1200	5	Harbor	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	12	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	33.8201	-118.3015
495	12/13/2022 12:00:00 AM	12/21/2020 12:00:00 AM	1800	9	Van Nuys	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	M	501.0	SINGLE FAMILY DWELLING	301.0	BELT FLAILING INSTRUMENT/CHAIN	AO	Adult Other	34.1939	-118.4313
556	10/30/2022 12:00:00 AM	06/01/2020 12:00:00 AM	1500	12	77th Street	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	10	F	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	33.9636	-118.2871
727	10/26/2022 12:00:00 AM	01/01/2020 12:00:00 AM	1200	17	Devonshire	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	34.2880	-118.4971
750	05/09/2023 12:00:00 AM	01/09/2020 12:00:00 AM	1	10	West Valley	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	3	F	502.0	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	34.1939	-118.4924
771	01/18/2023 12:00:00 AM	12/01/2020 12:00:00 AM	1200	21	Topanga	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	14	F	501.0	SINGLE FAMILY DWELLING	500.0	UNKNOWN WEAPON/OTHER WEAPON	AO	Adult Other	34.2393	-118.6090
855	07/30/2022 12:00:00 AM	07/01/2020 12:00:00 AM	1200	16	Foothill	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	12	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	34.2139	-118.3703
887	10/17/2022 12:00:00 AM	09/01/2020 12:00:00 AM	1200	13	Newton	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	13	F	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	33.9993	-118.2432
996	05/02/2022 12:00:00 AM	11/01/2020 12:00:00 AM	1200	3	Southwest	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	10	F	501.0	SINGLE FAMILY DWELLING	500.0	UNKNOWN WEAPON/OTHER WEAPON	AA	Adult Arrest	34.0098	-118.3242

	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Crm Cd	Crm Cd Desc	Vict Age	Vict Sex	Premis Cd	Premis Desc	Weapon Used Cd	Weapon Desc	Status	Status Desc	LAT	LON
207	02/28/2023 12:00:00 AM	02/17/2020 12:00:00 AM	12:00	19	Mission	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	F	501.0	SINGLE FAMILY DWELLING	301.0	BELT FLAILING INSTRUMENT/CHAIN	AA	Adult Arrest	34.2337	-118.4553
246	02/01/2022 12:00:00 AM	02/01/2020 12:00:00 AM	12:00	5	Harbor	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	12	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	33.8201	-118.3015
495	12/13/2022 12:00:00 AM	12/21/2020 12:00:00 AM	18:00	9	Van Nuys	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	M	501.0	SINGLE FAMILY DWELLING	301.0	BELT FLAILING INSTRUMENT/CHAIN	AO	Adult Other	34.1939	-118.4313
556	10/30/2022 12:00:00 AM	06/01/2020 12:00:00 AM	15:00	12	77th Street	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	10	F	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	33.9636	-118.2871
727	10/26/2022 12:00:00 AM	01/01/2020 12:00:00 AM	12:00	17	Devonshire	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	34.2880	-118.4971
750	05/09/2023 12:00:00 AM	01/09/2020 12:00:00 AM	0:01	10	West Valley	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	3	F	502.0	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	34.1939	-118.4924
771	01/18/2023 12:00:00 AM	12/01/2020 12:00:00 AM	12:00	21	Topanga	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	14	F	501.0	SINGLE FAMILY DWELLING	500.0	UNKNOWN WEAPON/OTHER WEAPON	AO	Adult Other	34.2393	-118.6090
855	07/30/2022 12:00:00 AM	07/01/2020 12:00:00 AM	12:00	16	Foothill	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	12	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	34.2139	-118.3703
887	10/17/2022 12:00:00 AM	09/01/2020 12:00:00 AM	12:00	13	Newton	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	13	F	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	33.9993	-118.2432
996	05/02/2022 12:00:00 AM	11/01/2020 12:00:00 AM	12:00	3	Southwest	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	10	F	501.0	SINGLE FAMILY DWELLING	500.0	UNKNOWN WEAPON/OTHER WEAPON	AA	Adult Arrest	34.0098	-118.3242

	DATETIME OCC	Date Rptd	DATE OCC	TIME OCC	AREA	AREA NAME	Crm Cd	Crm Cd Desc	Vict Age	Vict Sex	Premis Cd	Premis Desc	Weapon Used Cd	Weapon Desc	Status	Status Desc	LAT	LON
207	02/17/2020 12:00	02/28/2023 12:00:00 AM	02/17/2020 12:00:00 AM	12:00	19	Mission	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	F	501.0	SINGLE FAMILY DWELLING	301.0	BELT FLAILING INSTRUMENT/CHAIN	AA	Adult Arrest	34.2337	-118.4553
246	02/01/2020 12:00	02/01/2022 12:00:00 AM	02/01/2020 12:00:00 AM	12:00	5	Harbor	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	12	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	33.8201	-118.3015
495	12/21/2020 18:00	12/13/2022 12:00:00 AM	12/21/2020 12:00:00 AM	18:00	9	Van Nuys	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	M	501.0	SINGLE FAMILY DWELLING	301.0	BELT FLAILING INSTRUMENT/CHAIN	AO	Adult Other	34.1939	-118.4313
556	06/01/2020 15:00	10/30/2022 12:00:00 AM	06/01/2020 12:00:00 AM	15:00	12	77th Street	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	10	F	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	33.9636	-118.2871
727	01/01/2020 12:00	10/26/2022 12:00:00 AM	01/01/2020 12:00:00 AM	12:00	17	Devonshire	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	5	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	34.2880	-118.4971
750	01/09/2020 00:01	05/09/2023 12:00:00 AM	01/09/2020 12:00:00 AM	0:01	10	West Valley	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	3	F	502.0	MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	34.1939	-118.4924
771	12/01/2020 12:00	01/18/2023 12:00:00 AM	12/01/2020 12:00:00 AM	12:00	21	Topanga	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	14	F	501.0	SINGLE FAMILY DWELLING	500.0	UNKNOWN WEAPON/OTHER WEAPON	AO	Adult Other	34.2393	-118.6090
855	07/01/2020 12:00	07/30/2022 12:00:00 AM	07/01/2020 12:00:00 AM	12:00	16	Foothill	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	12	M	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	AO	Adult Other	34.2139	-118.3703
887	09/01/2020 12:00	10/17/2022 12:00:00 AM	09/01/2020 12:00:00 AM	12:00	13	Newton	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	13	F	501.0	SINGLE FAMILY DWELLING	400.0	STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)	IC	Invest Cont	33.9993	-118.2432
996	11/01/2020 12:00	05/02/2022 12:00:00 AM	11/01/2020 12:00:00 AM	12:00	3	Southwest	627	CHILD ABUSE (PHYSICAL) - SIMPLE ASSAULT	10	F	501.0	SINGLE FAMILY DWELLING	500.0	UNKNOWN WEAPON/OTHER WEAPON	AA	Adult Arrest	34.0098	-118.3242

Child Abuse in Los Angeles (2020-2023)

Done by:- Narendra Rajendra Rane¶

Introduction¶

Why is this important?¶

Data Collection¶

Tools and Environment¶

Data Processing¶

Steps in Data Processing:¶

Data Formatting¶

Steps in Data Formatting:¶

Why Is This Important?¶

External Resources:¶

Date and Time Processing¶

Steps in Date and Time Processing:¶

Why Is This Important?¶

External Resources:¶

Exploratory Analysis and Data Visualization¶

Steps in Analysis:¶

Insights from the Visualization:¶

Why Is This Important?¶

External Resources:¶

Monthly Analysis and Visualization¶

Key Steps:¶

Visualization:¶

Significance:¶

External Resources:¶

Monthly Trends Visualization¶

Key Steps:¶

Visualization:¶

Significance:¶

External Resources:¶

Analysis of Victim Gender Distribution Over the Years¶

Key Steps:¶

Insights from the Visualization:¶

Significance:¶

External Resources:¶

Victim Gender Distribution: Male vs Female¶

Key Steps:¶

Insights from the Visualization:¶

Significance:¶

External Resources:¶

Year-wise Distribution of Victim Age (Male & Female, Under 18)¶

Key Steps:¶

Insights from the Visualization:¶

Significance:¶

External Resources:¶

Distribution of Victim Age by Victim Sex (Under 18)¶

Key Steps:¶

Insights from the Visualization:¶

Significance:¶

External Resources:¶

Geospatial Visualization of Child Abuse Incidents in Los Angeles (2020-2023)¶

Key Steps:¶

Insights from the Map:¶

Significance:¶

External Resources:¶

Model: Analysis, Hypothesis Testing, and Machine Learning¶

Key Steps:¶

Insights from the Visualization:¶

Significance:¶

External Resources:¶

Descriptive Statistics and Gender-wise Trends¶

Key Steps:¶

Insights from the Visualization:¶

Significance:¶

External Resources:¶

Hypothesis Testing: Comparing Ages by Gender¶

Key Steps:¶

Insights from the Test:¶

Significance:¶

External Resources:¶

Hypothesis Testing: Analyzing Age Trends Over Time¶

Key Steps:¶

Insights from the Test:¶

Significance:¶

External Resources:¶

Regression Analysis on Victim Age by Gender¶

Key Steps:¶

Results:¶

Significance:¶